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Ricercatore t.d. art. 24 c. 3 lett. B
Dipartimento di Scienze e Metodi dell'Ingegneria

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2023 - A Review and Analysis of Traffic Data Sources [Relazione in Atti di Convegno]
Balugani, Elia; Marinello, Samuele; Gamberini, Rita; Butturi, MARIA ANGELA

Transportation is essential for economic and social development, and vehicle flow data can be used for safety monitoring, pollution analysis, and traffic flow management. Unfortunately, traffic management and control centres do not always comply with codified standards, making it difficult to obtain up-To-date data. This paper analyses open traffic datasets and Italian public traffic data sources available online, providing a knowledge base for transportation managers and researchers. Open traffic datasets are dimensionality-reduced and clustered. An event with 209,135 visitors is used to benchmark the public data sources, the time series of traffic flows are decomposed and a regression tree is used to identify different periods. The results suggest that the available Italian sensor grid is not fine enough to identify all incoming and outgoing traffic, more infrastructure investments are required or the available measurements should be coupled with other evaluation approaches capable of extending the punctual data through mathematical means.

2023 - Indoor Environmental Quality (IEQ): A Comparison between TOPSIS- and PROMETHEE-Based Approaches for Indirect Eliciting of Category Weights [Articolo su rivista]
Lolli, F.; Coruzzolo, A. M.; Balugani, E.

Indoor Environmental Quality (IEQ) has received a great deal of attention in recent years due to the relationship between worker comfort and productivity. Many academics have studied IEQ from both a building design and an IEQ assessment perspective. This latter line of research has mostly used direct eliciting to obtain weights assigned to IEQ categories such as thermal comfort, visual comfort, acoustic comfort, and indoor air quality. We found only one application of indirect eliciting in the literature. Such indirect eliciting operates without the need for imprecise direct weighing and requires only comfort evaluations, which is in line with the Industry 5.0 paradigm of individual, dynamic, and integrated IEQ evaluation. In this paper, we use a case study to compare the only indirect eliciting model already applied to IEQ, based on TOPSIS, to an indirect eliciting method based on PROMETHEE and to a classical direct eliciting method (AHP). The results demonstrate the superiority of indirect eliciting in reconstructing individual preferences related to perceived global comfort.

2023 - Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing [Articolo su rivista]
Coruzzolo, A. M.; Lolli, F.; Balugani, E.; Magnani, E.; Sellitto, M. A.

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm.

2022 - A Decision Support System for the Selection of Insulating Material in Energy Retrofit of Industrial Buildings: A New Robust Ordinal Regression Approach [Articolo su rivista]
Lolli, F.; Balugani, E.; Butturi, M. A.; Coruzzolo, A. M.; Ishizaka, A.; Marinelli, S.; Romano, V.

The criteria for selecting insulating materials in the energy retrofitting of industrial buildings can often be conflicting, leading to a multicriteria decision-making problem. This is the first study to take an indirect elicitation approach to solving this selection problem, which is particularly applicable in the preliminary phases of negotiation with all of the decision-makers involved. We introduce a nonlinear indirect elicitation approach for PROMETHEE II that uses Bézier curves as nonlinear preference curves to fit the decision-maker's preferences, i.e., indifference and/or strict preferences for insulating materials that are taken as references. In our approach, no parameters need to be initially set, and thus, it has the advantage of setting both the preference curves on the criteria and the criteria weights when the decision-maker is not confident. The set of Bézier curves and criteria weights that best fits the preferences given by the decision-maker may thus be achieved and visualized, which provides managerial insights as it makes explicit the preference structure of the decision-maker. We use a case study to validate our proposal in a real setting and confirm that linear preference curves would have achieved less clear relations between the insulating materials used as references respect to Bèzier curves.

2022 - On demand printing with Additive Manufacturing (AM) for spare parts: scenarios for the insourcing of a 3D Printer [Relazione in Atti di Convegno]
Coruzzolo, A. M.; Lolli, F.; Balugani, E.; Rimini, B.

Additive Manufacturing (AM) has become a promising technique for spare parts management. The reduced lead time of AM compared to Classical Manufacturing (CM) has attracted the interest of researchers and many applications of AM to spare parts management have been introduced in the literature. However, the high production and equipment costs obscure the advantages of AM to spare parts management to practitioners and academics. The recent literature on spare parts management with AM have two main limitations which we address in this work. The first is that AM spare parts are mistakenly assumed to be less reliable than CM ones, which has been refuted by the recent literature on the mechanical characteristics of AM parts. Secondly, the external supply of AM parts that excludes the investment cost of the equipment. Our model overcomes these limitations by taking into account a spare part installed on a fleet of systems which failures are based on failure data from recent literature. In addition, we consider an insourced 3D printer, and account for the purchasing cost. We propose several scenarios for the insourcing of a 3D printing, considering a future cost reduction and constrained stock systems, individuating constrained stock system with high lead times for the CM part, ideal for in-house printing. The work has been supported by the project SUPERCRAFT, funded by the Emilia-Romagna Region (Italy) with European funds (POR FESR).

2022 - Spare parts management with Additive Manufacturing (AM): a critical review [Relazione in Atti di Convegno]
Coruzzolo, A. M.; Balugani, E.; Gamberini, R.

Additive Manufacturing (AM) is a promising technology for producing spare parts, due to the wide variety of forms and materials that can be used and their enhanced mechanical properties. Given these features and the low lead times compared to classical manufacturing (CM), AM is now being investigated for the management of spare parts. This literature stream is relatively new, with many works based on different hypotheses (e.g., the reliability of AM parts) and with different conclusions. This critical literature review provides practitioners with information on the models available, their findings, and their limitations. Further research directions are also identified.

2022 - The Indoor Environmental Quality: A TOPSIS-based approach with indirect elicitation of criteria weights [Articolo su rivista]
Lolli, F.; Maria Coruzzolo, A.; Balugani, E.

The Indoor Environmental Quality (IEQ) assessment is a hot topic both for designers of industrial buildings and for academics since it has been proven to affect workers’ productivity. Despite the advantages of indirect eliciting approaches, only direct eliciting is used in the literature to assign weights to the main risks included in the IEQ assessment, i.e., those referring to the thermal comfort, visual comfort, acoustic comfort and indoor air quality. In order to bridge this gap and in line with the drivers of the human-centric industrial revolution, we have developed an indirect eliciting approach based on logistic regression and integer optimization that indirectly derives the aforementioned weights per worker (i.e., individual weighting) on the basis of the overall comfort perceived by him/her in different reference scenarios. These weights are then used to compute a TOPSIS-based risk measure that maps the aggregated, individual and dynamic risks to which the worker is subjected over time. A real case study is used to validate our proposal. The achieved results highlight the superiority of our indirect eliciting approach compared to the Analytical Hierarchic Process in reconstructing the overall comfort perceived by workers, as well as that age plays a crucial role to assign weights to the main risks included in the IEQ.

2022 - Towards Smart Cities for Tourism: the POLIS-EYE Project [Relazione in Atti di Convegno]
Seravalli, Alessandro; Busani, Mariaelena; Venturi, Simone; Brutti, Arianna; Petrovich, Carlo; Frascella, Angelo; Paolucci, Fabrizio; Di Felice, Marco; Lombardi, Michele; Bellodi, Elena; Zese, Riccardo; Bertasi, Francesco; Balugani, Elia; Cecaj, Alket; Gamberini, Rita; Mamei, Marco; Picone, Marco

Novel and widespread ICT and Internet of Things (IoT) technology can provide fine-grained real-time information to the tourist sector, both to support the demand side (tourists) and the supply side (managers and organizers). We present the POLIS-EYE project that aims to build decision-support systems helping tourist-managers to organize and optimize policies and resources. In particular, we focus on a service to monitor and forecast people presence in tourist areas by combining heterogeneous datasets with a special focus on data collected from the mobile phone network.

2021 - Coffee capsule impacts and recovery techniques: A literature review [Articolo su rivista]
Marinello, S.; Balugani, E.; Gamberini, R.

The recently developing coffee market has been characterized by profound changes caused by new solutions and technologies for coffee preparation. The polylaminate materials that compose most popular capsules make them a type of waste that is difficult to manage and recycle. This paper analyses the scientific references that deal with studying and improving the management processes of waste coffee capsules, as well as the studies that have analysed their environmental impact. Through a bibliographic review, some encouraging aspects emerged in the recovery of materials that can be adequately recycled (plastics and metals), as well as their possible use for the production of biogas and energy recovery. The need to manually separate the components that make up the capsule still represents one of the main challenges. Many efforts are still needed to favour the environmental sustainability of this waste from a strategic, technological and consumer empowerment point of view.

2021 - Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand [Articolo su rivista]
Sgarbossa, Fabio; Peron, Mirco; Lolli, Francesco; Balugani, Elia

Due to the main peculiarities of spare parts, i.e. intermittent demands, long procurement lead times and high downtime costs when the parts are not available on time, it is often difficult to find the optimal inventory level. Recently, Additive Manufacturing (AM) has emerged as a promising technique to improve spare parts inventory management thanks to a ‘print on demand’ approach. So far, however, the impact of AM on spare parts inventory management has been little considered, and it is not yet clear when the use of AM for spare parts inventory management would provide benefits over Conventional Manufacturing (CM) techniques. With this paper we thus aim to contribute to the field of AM spare parts inventory management by developing decision trees that can be of support to managers and practitioners. To this aim, we considered a Poisson-based inventory management system and we carried out a parametrical analysis considering different part sizes and complexity, backorder costs and part consumption. Moreover, we evaluated scenarios where the order-up-to level is limited to resemble applications with a limited storage capacity. For the first time, the analysis was not limited to just one AM and one CM technique, but several AM and CM techniques were considered, also combined with different post-process treatments, for a total of nine different sourcing alternatives. In addition, the economic and technical performance of the different sourcing options were obtained thanks to an interdisciplinary approach, where experts from production economics and material science were brought together.

2021 - Dimensionality reduced robust ordinal regression applied to life cycle assessment [Articolo su rivista]
Balugani, E.; Lolli, F.; Pini, M.; Ferrari, A. M.; Neri, P.; Gamberini, R.; Rimini, B.

Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Robust Ordinal Regression is thus a versatile method of reducing the cognitive effort required by decision makers for eliciting their preference structures in Life Cycle Assessment, although it does not directly operate on noisy alternatives and requires Stochastic Multicriteria Acceptability Analysis to deal with such scenarios. We propose integrating a dimensionality reduction technique, Principal Component Analysis, and Robust Ordinal Regression methods, to reduce the problem dimensionality and ensure the actual problem features are considered. A generated dataset, a dataset from literature and a Life Cycle Assessment case study are used to test the effectiveness of the proposed methods.

2021 - Sustainability of logistics infrastructures: operational and technological alternatives to reduce the impact on air quality [Relazione in Atti di Convegno]
Marinello, S.; Balugani, E.; Rimini, B.

Modern ports are productive systems characterized by transport-type activities (of goods and people) and by activities typically related to the sectors of industry, construction, commerce and related services. Despite their fundamental role in the economic and social development of the local area, ports also have a negative impact on the environment. This paper analyses the effect on the air quality of a maritime container terminal by assessing the typical activities carried out there. Five scenarios were studied using an EMEP/EEE (2019) bottom-up air pollutant inventory approach and through air quality numerical simulations with the ADMS-5 model. Changes in the layout of where the activities are carried out, the use of cold ironing, and the use of LNG as a fuel are the scenarios compared with the "BASE" condition. The results highlighted the improved air quality due to each solution, demonstrating how the use of alternative fuels or the electrification of the docks reduces pollutants by more than 70-80%. Delocalizing some of the handling was found to have fewer benefits. Economic factors and the engagement of key stakeholders would seem to influence the diffusion of these solutions.

2021 - The Dynamic, Individual and Integrated Risk Assessment: A Multi-criteria Approach Using Big Data [Relazione in Atti di Convegno]
Lolli, Francesco; Coruzzolo, ANTONIO MARIA; D'Alessandro, Giulia; Balugani, Elia; Butturi, MARIA ANGELA; Marinello, Samuele; Marinelli, Simona

Occupational Health and Safety Risk Assessment can undoubtedly benefit from enabling technologies of Industry 4.0, with the aim of collecting and analyzing the big data related to the occupational risk factors arising into workplaces. In this paper, the assessment of the occupational risk is addressed by means of a multi-criteria approach. Indeed, after the pre-treatment of the time series of the said risk factors by means of a segmentation algorithm, a TOPSIS approach is implemented to assess the dynamic, individual and integrated risk to which a worker is subjected over the time. Finally, a numerical example is reported to illustrate the proposed in practice.

2021 - Understanding the Demand Driven Material Requirements Planning Scope of Application: a Critical Literature Review [Relazione in Atti di Convegno]
Butturi, MARIA ANGELA; De Rosa, Giuseppina; Balugani, Elia; Gamberini, Rita

The supply chains complexity generated by the dynamic market demand imposes the improvement of the classical production control systems. A recently introduced method, the Demand Driven Material Requirements Planning (DDMRP), is proposed as an upgrade of the Material Requirements Planning (MRP), widely used in industry, capable of overcoming the nervousness of MRP environment and the bullwhip effect affecting supply chains under uncertainties. The DDMRP approach, however, is still not well established since the conditions for its application are still little investigated. Thus, this study aims at reviewing the existing scientific literature concerning DDMRP method in order to critically analyse its main scope of application as well as its real practical performance. From the reviewed literature three main research lines emerged: DDMRP basic principles, comparison with other methodologies, and case studies. The analysis of both research oriented papers and case studies points out some critical issues that are limiting the diffusion of the DDMRP method, including the additional costs necessary to adapt the in use control and planning software. The main criticality of the method is recognized to be the high subjectivity affecting the positioning of the buffers, and the need for classifying the suitable sectors of application.

2020 - A model for renewable energy symbiosis networks in eco-industrial parks [Relazione in Atti di Convegno]
Butturi, Maria Angela; Sellitto, Miguel A:; Lolli, Francesco; Balugani, Elia; Neri, Alessandro

Renewable energy technologies integration within industrial districts can boost carbon emissions reduction in the industry sector. The eco-industrial parks model promotes the sustainable use of energy and the application of energy synergies and energy exchanges that can include renewable sources of energy. This paper presents an optimization methodology based on a multi-stakeholder perspective to evaluate energy symbiosis including the integration of renewable energy sources within the parks. The study results in three scenarios providing to managers of single firms and parks relevant information for supporting decision making regarding the economic sustainability and the environmental impacts of the energy synergies. The results show that the optimization of the collective point of view ensures more efficient management of the energy supplied by renewables as well as by firms that can provide an energy surplus.

2020 - Empirical Evaluation of the Impact of Resilience and Sustainability on Firms’ Performance [Articolo su rivista]
Balugani, Elia; Butturi, Maria Angela; Chevers, Delroy; Parker, David; Rimini, Bianca

The concepts of resilience and sustainability appear multi-dimensional and correlated, depending on the context. Operational sustainability practices can enhance the resilience of a firm, and support its growth. This study aims at analyzing the impact of a sustainability strategy, measured by means of a sustainability maturity index (SMI), on the financial performance of a company. Since the SMI is strictly correlated to resilience capabilities, the performed analysis represents a first level integration of the sustainability and resilience indicators in a common framework. A data sample from 53 organizations was collected through structured interviews and analyzed to identify possible relationships between the SMI and the financial performance indexes. The analysis does not support commonly reported arguments: we show that profitability does not show a significant relationship with sustainable strategic intent. Interestingly, firm country of origin, size of the organization, and market focus, likewise, do not have a significant relationship with SMI. Arguably, multi-dimensional company performance, including both financial and non-financial measures, should be considered to assess the impact of sustainability practices. Moreover, further investigations are needed to capture firms’ nonfinancial indicators of performance that are related to sustainability and resilience, for building up a unified framework enabling trade-off analysis.

2020 - Environmental benefits of the industrial energy symbiosis approach integrating renewable energy sources [Relazione in Atti di Convegno]
Marinelli, S; Butturi, M. A.; Balugani, E.; Lolli, F.; Rimini, B.

Industry sector accounts for almost 40% of final energy demand and is responsible for one-fifth of global energy-related CO₂ emissions. A viable pathway to reduce the carbon footprint of the industry sector is represented by the industrial energy symbiosis, that promotes inter-firm energy exchanges and the sharing of energy-related resources. While a single firm comes across technical and financial barriers that often hamper the implementation of energy conservation projects, the cooperation between firms can enable energy saving measures and the use of renewable energy sources at industry level. Considering a case study involving an energy intensive industry, the study analyses the potential environmental benefits of the industrial energy symbiosis approach integrating renewable energy sources. The research suggests a methodology to design strategic energy symbiosis connections, advantageous for the involved firms, with the objective of reducing carbon emissions and economic costs. The methodology is based on the mathematical optimization through mixed integer linear programming. combined with the environmental analysis conducted with the life-cycle assessment method. The application of the methodology to the case study provides a scenario outlining all the potential energy flows, that are evaluated respect to the state-of-the-art (reference) scenario and alternative electrification strategies, showing the potential environmental benefits.

2020 - Logistic regression for criteria weight elicitation in PROMETHEE-based ranking methods [Relazione in Atti di Convegno]
Balugani, Elia; Lolli, Francesco; Butturi, MARIA ANGELA; Ishizaka, Alessio; Afonso Sellitto, Miguel

For a PROMETHEE II method used to rank concurrent alternatives both preference functions and weights are required, and if the weights are unknown, they can be elicited by leveraging present or past partial rankings. If the known partial ranking is incorrect, the eliciting methods are ineffective. In this paper a logistic regression method for weight elicitation is proposed to tackle this scenario. An experiment is carried out to compare the logistic regression method performance against a state-of-the-art linear weight elicitation method, proving the validity of the proposed methodology.

2019 - A periodic inventory system of intermittent demand items with fixed lifetimes [Articolo su rivista]
Balugani, Elia; Lolli, Francesco; Gamberini, Rita; Rimini, Bianca; Babai, M. Z.

Perishable items with a limited lifespan and intermittent/erratic consumption are found in a variety of industrial settings: dealing with such items is challenging for inventory managers. In this study, a periodic inventory control system is analysed, in which items are characterised by intermittent demand and known expiration dates. We propose a new inventory management method, considering both perishability and intermittency constraints. The new method is a modification of a method proposed in the literature, which uses a periodic order-up-to-level inventory policy and a compound Bernoulli demand. We derive the analytical expression of the fill rate and propose a computational procedure to calculate the optimal solution. A comparative numerical analysis is conducted to evaluate the performance of the proposed solution against the standard inventory control method, which does not take into account perishability. The proposed method leads to a bias that is only affected by demand size, in contrast to the standard method which is impacted by more severe biases driven by intermittence and periods before expiration.

2019 - Complexity measurement in two supply chains with different competitive priorities [Relazione in Atti di Convegno]
Sellitto, M. A.; Lolli, F.; Rimini, B.; Balugani, E.

Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity.

2019 - Corrigendum to “Preparation for reuse activity of waste electrical and electronic equipment: Environmental performance, cost externality and job creation” (Journal of Cleaner Production (2019) 222 (77–89), (S0959652619306870), (10.1016/j.jclepro.2019.03.004)) [Articolo su rivista]
Pini, M.; Lolli, F.; Balugani, E.; Gamberini, R.; Neri, P.; Rimini, B.; Ferrari, A. M.

The authors regret some errors with the notation of decimals in tables 8, 11, 12, 13 and 14. Following, the authors report the correct number values per each of the above mentioned tables. Table 8 Repair time spent for preparing for reuse of WEEE.

2019 - Cost-benefit evaluation of investment in natural gas distribution [Relazione in Atti di Convegno]
Balugani, Elia; Butturi, MARIA ANGELA; Lolli, Francesco; Rimini, Bianca

Investment in the distribution of natural gas must be assessed by combining a technical analysis of the investment and an assessment of the social costs and benefits, to evaluate the impact of the project on social welfare in monetary terms. This paper describes how such an analysis can be conducted, by developing a methodology for the evaluation of investment in the distribution of natural gas. Once the net social benefit (NSB) of the investment has been evaluated, it is also important to assess the degree of reliability of such an estimate. This assessment can be conducted through two types of tests: sensitivity analysis and risk analysis. The critical variables are identified in sensitivity analysis as those that have a significant impact on the predicted outcome when they change. To address any uncertainties in the critical variables, a risk analysis quantifies the probability that the NSB is less than that estimated when using modal values for the critical variables. This type of analysis, combined with a technical evaluation, can be effectively used to assess the social consequences of an investment.

2019 - Machine learning for multi-criteria inventory classification applied to intermittent demand [Articolo su rivista]
Lolli, F.; Balugani, E.; Ishizaka, A.; Gamberini, R.; Rimini, B.; Regattieri, A.

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.

2019 - On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application [Articolo su rivista]
Lolli, Francesco; Balugani, Elia; Ishizaka, Alessio; Gamberini, Rita; Butturi, Maria Angela; Marinello, Samuele; Rimini, Bianca

Today, almost everybody has a smartphone and applications have been developed to help users to take decisions (e.g. which hotel to choose, which museum to visit, etc). In order to improve the recommendations of the mobile application, it is crucial to elicit the preference structures of the user. As problems are often based on several criteria, multicriteria decision aiding methods are most adequate in these cases, and past works have proposed indirect eliciting approaches for multicriteria decision aiding methods. However, they often do not aim of reducing as much as possible the cognitive efforts required by the user. This is prerequisite of mobile applications as they are used by everybody. In this work, the weights to assign to the evaluation criteria in a PROMETHEE-based ranking approach are unknown, and therefore must be elicited indirectly either from a partial ranking provided by the user or from the selection of his/her most preferred alternative into a subset of reference alternatives. In the latter case, the cognitive effort required by the decision-maker is minimal. Starting from a linear optimisation model aimed at searching for the most discriminating vector of weights, three quadratic variants are proposed subsequently to overcome the issues arising from the linear model. An iterative quadratic optimisation model is proposed to fit the real setting in which the application should operate, where the eliciting procedure must be launched iteratively and converge over time to the vector of weights, which are the weights that the user implicitly assigns to the evaluation criteria. Finally, three experiments are performed to confirm the effectiveness and the differences between the proposed models.

2019 - Preparation for reuse activity of waste electrical and electronic equipment: Environmental performance, cost externality and job creation [Articolo su rivista]
Pini, Martina; Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Neri, Paolo; Rimini, Bianca; Ferrari, Anna Maria

The European Waste Electrical and Electronic Equipment system introduced measures to encourage both the reduction of the amount of electronic waste and its separation to prepare for reuse. The aim of this study is compare the environmental performance, cost externality and job creation of the whole life cycle of new and reconditioned electrical and electronic equipment by adopting Life Cycle Assessment methodology. Five electrical and electronic equipment categories were investigated and the data collection was made on an Italian context. The refurbishing of breakdown electrical and electronic equipment was assessed by considering different sets of faulty components (Scenario A and B) and a total of 25 scenarios were studied. Moreover, both attributional and consequential life cycle inventory modelling framework were adopted to represent the investigated scenarios. The outcomes highlighted that the preparation for reuse process leads to obtaining a sustainable electronic device than the new one, depending on which set of components are replaced. Adopting Scenario B with the attributional model, the environmental damage of reconditioned electrical and electronic equipment decreases compared to the new one. Conversely, the consequential approach determines an environmental credit for all repaired electronic devices except for one category; in particular, Scenario A produced the largest environmental advantage. The analyses of external costs and social aspects confirm that the preparation for reuse activity allows to obtain a more sustainable product than a new one. For these two latter aspects, the results showed a turnaround passing from attributional model to consequential one. Noting the variability in results adopting both different life cycle inventory modelling framework and set of replaced components, the Life Cycle Assessment practitioner, that conducted the study, should help the decision-makers to determine which scenario is more sustainable accomplishing an adequate choice.

2019 - P207 - VALETUDO project: VAlidation study of the LEarning machine Technique (neUral networks) for big Data in the breast tumor in ReggiO Emilia region, Italy [Poster]
Giovanardi, Filippo; Lolli, Francesco; Balugani, Elia; Pezzuolo, D.; Prati, G.; Degli Esposti, C.; Cerioli, D.

2019 - Quality cost-based allocation of training hours using learning-forgetting curves [Articolo su rivista]
Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Rimini, Bianca

The training of suppliers and inbound quality inspectors is a common strategy to increase the quality performance of the supply chain but, under budget constraints, these actors compete for a limited amount of training hours. The proposed model aims to allocate the available training hours so as to minimise a total quality cost function composed of prevention, appraisal, and failure costs; it also sets the inspection rates defining the inspection policies assigned to suppliers. The relationship between decision variables and costs is expressed through organisational and individual learning-forgetting curves, for suppliers and quality inspectors respectively, and the effect of the training hours on quality improvement is measured in terms of failure rates. To the best of our knowledge, a total quality cost model with such decision variables is new in the related literature, as it is a model including both organisational and individual learning-forgetting phenomena. A nonlinear optimisation approach was adopted to solve this complex problem. The experimental section includes a decision trees analysis of simplified scenarios in order to interpret the model functioning, as well as a complex numerical example to extrapolate managerial insights.

2019 - Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis [Articolo su rivista]
Butturi, M. A.; Lolli, F.; Sellitto, M. A.; Balugani, E.; Gamberini, R.; Rimini, B.

Replacing fossil fuels with renewable energy sources is considered as an effective means to reduce carbon emissions at the industrial level and it is often supported by local authorities. However, individual firms still encounter technical and financial barriers that hinder the installation of renewables. The eco-industrial park approach aims to create synergies among firms thereby enabling them to share and efficiently use natural and economic resources. It also provides a suitable model to encourage the use of renewable energy sources in the industry sector. Synergies among eco-industrial parks and the adjacent urban areas can lead to the development of optimized energy production plants, so that the excess energy is available to cover some of the energy demands of nearby towns. This study thus provides an overview of the scientific literature on energy synergies within eco-industrial parks, which facilitate the uptake of renewable energy sources at the industrial level, potentially creating urban-industrial energy symbiosis. The literature analysis was conducted by arranging the energy-related content into thematic categories, aimed at exploring energy symbiosis options within eco-industrial parks. It focuses on the urban-industrial energy symbiosis solutions, in terms of design and optimization models, technologies used and organizational strategies. The study highlights four main pathways to implement energy synergies, and demonstrates viable solutions to improve renewable energy sources uptake at the industrial level. A number of research gaps are also identified, revealing that the energy symbiosis networks between industrial and urban areas integrating renewable energy systems, are under-investigated.

2018 - A Fuzzy Logic Control application to the Cement Industry [Relazione in Atti di Convegno]
Sellitto, M. A.; Balugani, Elia; Gamberini, R.; Rimini, B.

A case study on continuous process control based on fuzzy logic and supported by expert knowledge is proposed. The aim is to control the coal-grinding operations in a cement manufacturing plant. Fuzzy logic is based on linguistic variables that emulate human judgment and can solve complex modeling problems subject to uncertainty or incomplete information. Fuzzy controllers can handle control problems when an accurate model of the process is unavailable, ill-defined, or subject to excessive parameter variations. The system implementation resulted in productivity gains and energy consumption reductions of 3% and 5% respectively, in line with the literature related to similar applications.

2018 - A human-machine learning curve for stochastic assembly line balancing problems [Relazione in Atti di Convegno]
Lolli, F.; Balugani, E.; Gamberini, R.; Rimini, B.; Rossi, V.

The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions.

2018 - Clustering for inventory control systems [Relazione in Atti di Convegno]
Balugani, E.; Lolli, F.; Gamberini, R.; Rimini, B.; Regattieri, A.

Inventory control is one of the main activities in industrial plant management. Both process owners and line workers interact daily with stocks of components and finite products, and an effective management of these inventory levels is a key factor in an efficient manufacturing process. In this paper the algorithms k-means and Ward's method are used to cluster items into homogenous groups to be managed with uniform inventory control policies. This unsupervised step reduces the need for computationally expensive inventory system control simulations. The performance of this methodology was found to be significant but was strongly impacted by the intermediate feature transformation processes.

2018 - DEASort: Assigning items with data envelopment analysis in ABC classes [Articolo su rivista]
Ishizaka, Alessio; Lolli, Francesco; Balugani, Elia; Cavallieri, Rita; Gamberini, Rita

Multi-criteria inventory classification groups similar items in order to facilitate their management. Data envelopment analysis (DEA) and its many variants have been used extensively for this purpose. However, DEA provides only a ranking and classes are often constructed arbitrarily with percentages. This paper introduces DEASort, a variant of DEA aimed at sorting problems. In order to avoid unrealistic classification, the expertise of decision-makers is incorporated, providing typical examples of items for each class and giving the weights of the criteria with the Analytic Hierarchy Process (AHP). This information bounds the possible weights and is added as a constraint in the model. DEASort is illustrated using a real case study of a company managing warehouses that stock spare parts.

2018 - Distributed renewable energy generation: a critical review based on the three pillars of sustainability [Relazione in Atti di Convegno]
Butturi, MARIA ANGELA; Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Rimini, Bianca

Reducing emissions responsible for the climate change is recognized as a strategic goal at European and global level. A higher deployment of renewable energy sources is considered as essential for a low-carbon transition, towards a more sustainable energy system. The 2030 Framework for Climate and Energy sets out the European Union target for 2030 of at least 27% for the share of renewable energy consumption. A high share of renewables requires a new flexible and integrated electricity system to ensure grid stability and match supply and demand. The advances in technologies for renewable electricity and heating production, efficient storage solutions, and advanced ICT allow flexible electrical infrastructures: distributed renewable energy generation is now widely recognized as the main pathway towards an effective integration of discontinuous sources into the energy system. The discussion on renewable energy sources introduction in the energy system has long been focused on technical, economic and policy issues, but the transition to a distributed renewable energy generation approach demands a change of perspective, considering a multi-sectoral sustainability view and the need for multi-stakeholder action. Purpose of this research is reviewing the more recent scientific papers on the distributed renewable energy generation approach, focusing on how all the three key sustainability dimensions, environmental, economic and social, are evaluated and managed in a multi-criteria perspective. The sustainability indicators suggested in literature are classified and discussed to build up an up-to-date and comprehensive set of sustainability related criteria, suitable for future research applications and for supporting decision making processes.

2018 - Economic order quantity and storage assignment policies [Relazione in Atti di Convegno]
D’Urso, Diego; Chiacchio, Ferdinando; Lucio Compagno, :; Lolli, Francesco; Balugani, Elia

The basic Harris’s lot size model dates back to 1913 (Harris, 1913), hence one century from its publication has been recently celebrated. Starting from the seminal work of Harris, a wide plethora of contributors has faced with the lot-sizing problem for fitting the basic model of the economic order quantity to several environments. In fact, the three key parameters constituting the basic model, i.e. the demand rate, the ordering costs, and the inventory holding costs, have been widely explored in order to relax the assumptions of the original model. However, to the best of the authors’ knowledge, the liaison between holding costs and warehouse management has not been completely addressed. The holding costs have been early considered for simplicity as primarily given by the cost of capital, and thus dependent solely on the average inventory on stock. Conversely, by including a more detailed supply chain costs contribution, the economic order quantity calculus appears depending on a recursive calculus process and on the storage assignment policy. In fact, different approaches of warehouse management, e.g. shared and dedicated storage, lead to highly variable distances to be covered for performing the missions. This leads to a total cost function, and consequently to optimum lot sizes, that are affected by the warehouse management. In this paper, this relationship has been made explicit in order to evaluate an optimal order quantity taking into account storage assignment policies.

2018 - Spare Parts Replacement Policy Based on Chaotic Models [Relazione in Atti di Convegno]
Sellitto, Miguel A.; Balugani, Elia; Lolli, Francesco

Poisson point processes are widely used to model the consumption of spare parts. However, when the items have very low consumption rates, the historical sample sizes are too small. This paper presents a modelling technique for spare parts policies in the case of items with a low consumption rate. We propose the use of chaotic models derived from the well-known chaotic processes logistic map and Hénon attractor to assess the behaviour of a set of five medium voltage motors supplying four drives in the rolling mill of a steelmaking plant. Supported by the chaotic models, we conclude that the company needs an additional motor to ensure full protection against shortages.

2017 - AHP-K-GDSS: A new sorting method based on AHP for group decisions [Relazione in Atti di Convegno]
Ishizaka, Alessio; Lolli, Francesco; Gamberini, Rita; Rimini, Bianca; Balugani, Elia

Some public buildings need for energy requalification intervention as they are responsible for a significant share of energy consumption and other related CO2 emissions. With tight budget constraints choices have to be made. To solve this problem a group sorting decision support system based on the analytic hierarchy process, the Kmeans algorithm has been developed. The system aims at sorting alternatives into ordered classes of importance. A case study carried out in an Italian municipality allowed us to verify the validity of our new method in a real setting.

2017 - Decision Trees for Supervised Multi-criteria Inventory Classification [Relazione in Atti di Convegno]
Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Balugani, Elia; Rimini, Bianca

A multi-criteria inventory classification (MCIC) approach based on supervised classifiers (i.e. decision trees and random forests) is proposed, whose training is performed on a sample of items that has been previously classified by exhaustively simulating a predefined inventory control system. The goal is to classify automatically the whole set of items, in line with the fourth industrial revolution challenges of increased integration of ICT into production management. A case study referring to intermittent demand patterns has been used for validating our proposal, and a comparison with a recent unsupervised MCIC approach has shown promising results.

2017 - FMECA-based optimization approaches under an evidential reasoning framework [Relazione in Atti di Convegno]
Lolli, F.; Gamberini, R.; Balugani, E.; Rimini, B.; Mai, Francesco

One of the major shortcomings of traditional failure modes, effects and criticality analysis is the absence of any interconnection between failure ranking and a procedure for selecting the most critical maintenance/improvement tasks to be carried out. This limits the potential of FMECA for implementation in real environments. In order to bridge this gap, three different 0-1 knapsack models have been formulated. The first aims to select the failures in order to maximise cost savings. The second enriches the selection problem by also taking into account the probabilities of solving the failures with a set of maintenance tasks. The third aims to select the maintenance tasks to maximise the expected profit. In particular, the last two models make use of an evidential reasoning framework to deal with the epistemic uncertainty related to these probabilities. A dataset from a manufacturer of lift winches has been used to validate this proposal, as well as to comment on the need for group decision support systems that are capable of converting the FMECA ranking into maintenance tasks in real environments.

2017 - Inventory control system for intermittent items with perishability [Relazione in Atti di Convegno]
Balugani, E.; Lolli, F.; Gamberini, R.; Rimini, B.

Perishable items, with a limited lifespan and a known expiration date, are found in a variety of industrial settings. From the food to the pharmaceutical industries, the supply chains specialize their inventory control systems to handle the added complexity. These efforts are enhanced when the items present also an intermittent consumption, characterized by frequent periods without demand mixed to highly variable positive demand events. In this paper, a novel periodic inventory control system aims at bridging the gap between these two product features, managing intermittent items with expiration dates. The proposed system performs a combinatorial analysis evaluating all the demand scenarios before and after an expiration date to measure the expected fill rate. An optimization algorithm then sets the order quantity, using mathematical properties of the system to define efficient search boundaries.

2017 - Requalifying public buildings and utilities using a group decision support system [Articolo su rivista]
Lolli, Francesco; Ishizaka, Alessio; Gamberini, Rita; Rimini, Bianca; Balugani, Elia; Prandini, Laura

Public buildings and utilities are responsible for a significant share of energy consumption and other related CO2 emissions. There is therefore an acute need for energy requalification interventions. Unfortunately, municipalities are under tight budget constraints, but decisions have to be made. A new hybrid group decision support system has been proposed in a bid to provide them with firm, transparent support. The system is based on a combination of the analytic hierarchy process, the K- means algorithm, and the 0-1 knapsack model. The first two methods aim at sorting alternatives into ordered classes of importance. To help in this task, the Bezier curve-fitting approach is used to construct the preference functions of decision-makers based on reference points. Then, the knapsack model selects the alternatives from the generated classes while complying with the budget constraints. A case study carried out in an Italian municipality allowed us to verify the validity of our new method in a real setting, and to highlight the advantages of an automatic sorting procedure in practice.

2017 - Single-hidden layer neural networks for forecasting intermittent demand [Articolo su rivista]
Lolli, Francesco; Gamberini, Rita; Regattieri, A.; Balugani, Elia; Gatos, T.; Gucci, S.

Managing intermittent demand is a vital task in several industrial contexts, and good forecasting ability is a fundamental prerequisite for an efficient inventory control system in stochastic environments. In recent years, research has been conducted on single-hidden layer feedforward neural networks, with promising results. In particular, back-propagation has been adopted as a gradient descent-based algorithm for training networks. However, when managing a large number of items, it is not feasible to optimize networks at item level, due to the effort required for tuning the parameters during the training stage. A simpler and faster learning algorithm, called the extreme learning machine, has been therefore proposed in the literature to address this issue, but it has never been tried for forecasting intermittent demand. On the one hand, an extensive comparison of single-hidden layer networks trained by back-propagation is required to improve our understanding of them as predictors of intermittent demand. On the other hand, it is also worth testing extreme learning machines in this context, because of their lower computational complexity and good generalisation ability. In this paper, neural networks trained by back-propagation and extreme learning machines are compared with benchmark neural networks, as well as standard forecasting methods for intermittent demand on real-time series, by combining different input patterns and architectures. A statistical analysis is then conducted to validate the best performance through different aggregation levels. Finally, some insights for practitioners are presented to improve the potential of neural networks for implementation in real environments.

2017 - Stochastic assembly line balancing with learning effects [Relazione in Atti di Convegno]
Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Rimini, Bianca

Human learning is nowadays taken into account in several research fields, including the assembly line balancing problem. Despite the plethora of contributions and different approaches to solving the problem, the autonomous learning phenomenon, that is to say, the time-dependent or position-dependent reduction of assembly task times due to repetition, should also be explored using stochastic models which, to the best of our knowledge, have been disregarded. In this paper, a well-established cost-based stochastic balancing heuristic has been coupled with a time-dependent learning curve in order to investigate the role of learning in the rebalancing of assembly lines with repetitive tasks. Finally, a real case study has been conducted with the aim of demonstrating the applicability of our proposal.

2016 - Modelling production cost with the effects of learning and forgetting [Relazione in Atti di Convegno]
Lolli, Francesco; Messori, Michael; Gamberini, Rita; Rimini, Bianca; Balugani, Elia

Defining a dynamic model for calculating production cost is a challenging goal that requires a good fitting ability with real data over time. A novel cost curve is proposed here with the aim of incorporating both the learning and the forgetting phenomenon during both the production phases and the reworking operations. A single-product cost model is thus obtained, and a procedure for fitting the curve with real data is also introduced. Finally, this proposal is validated on a benchmark dataset in terms of mean square error.